Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Towards Learning and Explaining Indirect Causal Effects in Neural Networks
Authors: Abbavaram Gowtham Reddy, Saketh Bachu, Harsharaj Pathak, Benin Godfrey L, Varshaneya V, Vineeth N Balasubramanian, Satyanarayan Kar
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on synthetic and real-world datasets demonstrate that the causal effects learned by our ante-hoc method better approximate the ground truth effects compared to existing methods. |
| Researcher Affiliation | Collaboration | Abbavaram Gowtham Reddy1, Saketh Bachu1, Harsharaj Pathak1, Benin L. Godfrey1, Varshaneya V2, Vineeth N. Balasubramanian1, Satyanarayan Kar2 1 Indian Institute of Technology Hyderabad, India 2 Honeywell, Bengaluru, India |
| Pseudocode | Yes | Algorithm 1: Pseudocode for training N Ind model |
| Open Source Code | Yes | Code is available at https://github.com/gautam0707/Learning-and Explaining-Indirect-Causal-Effects. |
| Open Datasets | Yes | We conduct experiments on a synthetic dataset, three well-known real-world benchmark datasets, and three industry-based simulated datasets. ... Auto-MPG: In this experiment, we work on Auto-MPG dataset (Dua and Graff 2017) ... Lung Cancer: In Lung Cancer dataset (Scutari and Denis 2014), whose causal graph is known (see Appendix)... Sachs: Sachs dataset consists of 11 protein types and their causal relationships. |
| Dataset Splits | No | The paper mentions using a 'training set' and refers to 'test data point' but does not specify explicit percentages or counts for training, validation, and test splits needed to reproduce the experiment. |
| Hardware Specification | No | The paper mentions 'industry-grade flight simulator' for some datasets but does not provide specific hardware details such as GPU/CPU models, memory, or detailed computer specifications used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) required to replicate the experiment. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as concrete hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings in the main text. |